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Sample Size Estimation for Bio-Equivalence Trials Through Simulation

install.packages("SimTOST")
CRAN 30-day downloads, method
360+23.7% up on the 30 days before, the better direction
Test coverage, method
75%of lines run by tests

Sample size estimation for bio-equivalence trials is supported through a simulation-based approach that extends the Two One-Sided Tests (TOST) procedure. The methodology provides flexibility in hypothesis testing, accommodates multiple treatment comparisons, and accounts for correlated endpoints.

Full description

Sample size estimation for bio-equivalence trials is supported through a simulation-based approach that extends the Two One-Sided Tests (TOST) procedure. The methodology provides flexibility in hypothesis testing, accommodates multiple treatment comparisons, and accounts for correlated endpoints. Users can model complex trial scenarios, including parallel and crossover designs, intra-subject variability, and different equivalence margins. Monte Carlo simulations enable accurate estimation of power and type I error rates, ensuring well-calibrated study designs. The statistical framework builds on established methods for equivalence testing and multiple hypothesis testing in bio-equivalence studies, as described in Schuirmann (1987) <doi:10.1007/BF01068419>, Mielke et al. (2018) <doi:10.1080/19466315.2017.1371071>, Shieh (2022) <doi:10.1371/journal.pone.0269128>, and Sozu et al. (2015) <doi:10.1007/978-3-319-22005-5>. Comprehensive documentation and vignettes guide users through implementation and interpretation of results.

Maintainer
Thomas Debray
Latest release
9 Oct 2026
First release
18 Feb 2025

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More details
Releases
3
Needs compilation
Yes
Config/roxygen2/version
8.1.0
Config/testthat/edition
3
Subjects (1) Method, Subjects
Cite

Package

Run in R for the authors' preferred citation:

citation("SimTOST")
APA
Debray, T., Biogen Inc, Smart Data Analysis and Statistics B.V., Amaliah, D., Friede, T., McDonald, S., Mitroiu, M., Munoz, J., & Wei, W. (2026). SimTOST: Sample Size Estimation for Bio-Equivalence Trials Through
Simulation (Version 1.1.0) [Computer software]. https://doi.org/10.32614/CRAN.package.SimTOST
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This package has no citation file, so citation() returns this entry built from DESCRIPTION.

R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA
Balamuta, J. J. (2026). R Observatory: Metrics for SimTOST version 1.1.0 [Data set]. HJJB, LLC. Data release v2026-10-28. https://doi.org/10.5281/zenodo.21843040
About

From data release v2026-10-28, which the citation names so these numbers can be found later.

All 13 check platforms pass.

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  • 13 OK

Over time

No change since 8 Sep 2026

As a list (8)
ChangedWorst statusOKNotesWarningsErrors
8 Sep 2026OK13000
7 Sep 2026Error12001
5 Aug 2026OK13000
1 Aug 2026Note12100
2 May 2026OK13000
22 Apr 2026Note12200
18 Apr 2026Error11201
10 Mar 2026Note12200

Since March 2026

Check results on CRAN

Total downloads in the last 30 days
364+13.4% up on the 30 days before, the better directionIt is the sum of 3 channels: CRAN, r2u and autoCRAN.

CRAN rank

10,075 of 26,530

CRAN

360 downloads in the last 30 days

Rank
10,075 of 26,530
Change
+23.7% up on the 30 days before, the better direction

It is in the total. Take it out

r2u

2 downloads in the last 30 days

Rank
14,902 of 30,526
Change
−84.6% down on the 30 days before, the worse direction

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autoCRAN

2 downloads in the last 30 days

Rank
22,452 of 29,212
Change
−88.2% down on the 30 days before, the worse direction

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Over time

Compare downloads

Needs
6
Dependent packages
0
Suggested by
0
Needs (6)
Installing it adds
4 packages, 8.5 MB of source
Of those, need compilation
3
Optional extras (7)
Functions
172
Public exports
16
Lines of R code
8,831
Median complexity
7
Functions (172)
Size and composition

Lines by kind (20,710)

KindLinesShare
R code8,83143%
Tests2,98214%
Documentation3,39816%
Guides3,51717%
Compiled code1,98210%

Lines by language

LanguageLinesShare
R11,81357%
Rd3,39816%
Vignettes3,13715%
C++1,98210%
Other3802%

Languages are counted from file extensions.

Files
176
R functions
138
Compiled functions
34
Object systems
S3: 30 methods
Complexity
Median
7
Mean
12.7
Highest
150
Median lines per function
21
About complexity

Cyclomatic complexity counts the independent paths through a function: 1 is a straight line, higher is harder to test. R functions only.

Call graph figures
R functions
138
Calls
192
Unconnected
22
Groups
6
Growth (3 releases)
Total lines by release
7,478 on 18 Feb 202520,710 on 9 Oct 2026
Release changes

The tests ran and coverage was measured.

Lines run by tests
75.5%
Expressions run by tests
71.7%
Test cases
100
Exports named in tests
43.8%
Coverage by file (24)
Coverage run
Lines counted
5,489
Lines the tests ran
4,173
Tests passed
Yes
Measured with
covr 3.6.5 on R 4.6.1
Test suite
Has tests
Yes
Test frameworks
testthat
Test cases
100
Lines of tests
2,982
Test lines per line of code
0.34

Funded by Biogen Inc, Smart Data Analysis and Statistics B.V.

smartdata-analysis-and-statistics / simtost on github.com

Stars
3no change on 30 days ago
Forks
2
Open issues
1
Last commit
9 Oct 2026
Activity
Commits
182
Open pull requests
0
Tagged releases
2
Latest tagged release
v1.1-review, 7 Sep 2026
Issues closed in
28 days (median)
Pull requests closed in
–
Pull requests merged
–
Created
10 Oct 2024
Licence file reads as
Apache-2.0
Development practices (8)
KindFound
Continuous integrationGitHub Actions, R CMD check workflow
The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel
Docs sourceREADME.Rmd
GitHub Pages site last deployed 2026-10-09
The repository has a site deploy workflow
CRAN release processcran-comments.md, CRAN-SUBMISSION
Lint, format, editorRStudio project

Method, repository practices

Across smartdata-analysis-and-statistics

R releases
  • On CRAN
  • Release
VersionReleasedR
1.1.0 current9 Oct 20264.6.1
1.0.219 Feb 20254.4.2
1.0.118 Feb 20254.4.2
Release notes (1.1.0)

New features

  • The adjust = "t" option now supports Mielke's strong k-out-of-m adjustment.
  • Diagnostic and sample-size plots now support endpoint selection.
  • Count-outcome workflows now support Poisson and negative-binomial sensitivity analyses, with expanded documentation.

Minor improvements and fixes

  • update() now handles comparator, endpoint, mean, standard-deviation, and covariance information more consistently.
  • Negative-binomial dispersion scaling for aggregate counts in parallel designs is now correct.
  • Monte Carlo stability and precision diagnostics are improved.